OpenRaiser/Intern-Atlas huggingface dataset:
Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios
Venue: arXiv cs.RO 2024
Year: 2024

Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite the rapid development, most existing studies solely focused on normal safe scenarios while largely neglecting safety-critical scenarios, particularly those involving imminent collisions. This oversight may result in autonomous vehicles lacking the essential predictive ability in such situations, posing a significant threat to safety. To tackle these, this paper proposes a risk-aware trajectory prediction framework tailored to safety-critical scenarios. Leveraging distinctive hazardous features, we develop three core risk-aware components. First, we introduce a risk-incorporated scene encoder, which augments conventional encoders with quantitative risk information to achieve risk-aware encoding of hazardous scene contexts. Next, we incorporate endpoint-risk-combined intention queries as prediction priors in the decoder to ensure that the predicted multimodal trajectories cover both various spatial intentions and risk levels. Lastly, an auxiliary risk prediction task is implemented for the ultimate risk-aware prediction. Furthermore, to support model training and performance evaluation, we introduce a safety-critical trajectory prediction dataset and tailored evaluation metrics. We conduct comprehensive evaluations and compare our model with several SOTA models. Results demonstrate the superior performance of our model, with a significant improvement in most metrics. This prediction advancement enables autonomous vehicles to execute correct collision avoidance maneuvers under safety-critical scenarios, eventually enhancing road traffic safety.
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Venue: arXiv cs.LG 2024
Year: 2024

Dynamic Algorithm Configuration (DAC) addresses the challenge of dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on individual tasks. Agents trained with Deep Reinforcement Learning (RL) offer a pathway to solve such settings. However, the limited generalization performance of these agents has significantly hindered the application in DAC. Our hypothesis is that a potential bias in the training instances limits generalization capabilities. We take a step towards mitigating this by selecting a representative subset of training instances to overcome overrepresentation and then retraining the agent on this subset to improve its generalization performance. For constructing the meta-features for the subset selection, we particularly account for the dynamic nature of the RL agent by computing time series features on trajectories of actions and rewards generated by the agent's interaction with the environment. Through empirical evaluations on the Sigmoid and CMA-ES benchmarks from the standard benchmark library for DAC, called DACBench, we discuss the potentials of our selection technique compared to training on the entire instance set. Our results highlight the efficacy of instance selection in refining DAC policies for diverse instance spaces.
EaDeblur-GS: Event assisted 3D Deblur Reconstruction with Gaussian Splatting
Venue: arXiv cs.CV 2024
Year: 2024

3D deblurring reconstruction techniques have recently seen significant advancements with the development of Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Although these techniques can recover relatively clear 3D reconstructions from blurry image inputs, they still face limitations in handling severe blurring and complex camera motion. To address these issues, we propose Event-assisted 3D Deblur Reconstruction with Gaussian Splatting (EaDeblur-GS), which integrates event camera data to enhance the robustness of 3DGS against motion blur. By employing an Adaptive Deviation Estimator (ADE) network to estimate Gaussian center deviations and using novel loss functions, EaDeblur-GS achieves sharp 3D reconstructions in real-time, demonstrating performance comparable to state-of-the-art methods.
Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons
Venue: arXiv cs.NE 2024
Year: 2024

Thanks to their parallel and sparse activity features, recurrent neural networks (RNNs) are well-suited for hardware implementation in low-power neuromorphic hardware. However, mapping rate-based RNNs to hardware-compatible spiking neural networks (SNNs) remains challenging. Here, we present a $ΣΔ$-low-pass RNN (lpRNN): an RNN architecture employing an adaptive spiking neuron model that encodes signals using $ΣΔ$-modulation and enables precise mapping. The $ΣΔ$-neuron communicates analog values using spike timing, and the dynamics of the lpRNN are set to match typical timescales for processing natural signals, such as speech. Our approach integrates rate and temporal coding, offering a robust solution for the efficient and accurate conversion of RNNs to SNNs. We demonstrate the implementation of the lpRNN on Intel's neuromorphic research chip Loihi, achieving state-of-the-art classification results on audio benchmarks using 3-bit weights. These results call for a deeper investigation of recurrency and adaptation in event-based systems, which may lead to insights for edge computing applications where power-efficient real-time inference is required.
Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Arabic Optical Character Recognition (OCR) and Handwriting Recognition (HWR) pose unique challenges due to the cursive and context-sensitive nature of the Arabic script. This study introduces Qalam, a novel foundation model designed for Arabic OCR and HWR, built on a SwinV2 encoder and RoBERTa decoder architecture. Our model significantly outperforms existing methods, achieving a Word Error Rate (WER) of just 0.80% in HWR tasks and 1.18% in OCR tasks. We train Qalam on a diverse dataset, including over 4.5 million images from Arabic manuscripts and a synthetic dataset comprising 60k image-text pairs. Notably, Qalam demonstrates exceptional handling of Arabic diacritics, a critical feature in Arabic scripts. Furthermore, it shows a remarkable ability to process high-resolution inputs, addressing a common limitation in current OCR systems. These advancements underscore Qalam's potential as a leading solution for Arabic script recognition, offering a significant leap in accuracy and efficiency.
New Capability to Look Up an ASL Sign from a Video Example
Venue: arXiv cs.CV 2024
Year: 2024

Looking up an unknown sign in an ASL dictionary can be difficult. Most ASL dictionaries are organized based on English glosses, despite the fact that (1) there is no convention for assigning English-based glosses to ASL signs; and (2) there is no 1-1 correspondence between ASL signs and English words. Furthermore, what if the user does not know either the meaning of the target sign or its possible English translation(s)? Some ASL dictionaries enable searching through specification of articulatory properties, such as handshapes, locations, movement properties, etc. However, this is a cumbersome process and does not always result in successful lookup. Here we describe a new system, publicly shared on the Web, to enable lookup of a video of an ASL sign (e.g., a webcam recording or a clip from a continuous signing video). The user submits a video for analysis and is presented with the five most likely sign matches, in decreasing order of likelihood, so that the user can confirm the selection and then be taken to our ASLLRP Sign Bank entry for that sign. Furthermore, this video lookup is also integrated into our newest version of SignStream(R) software to facilitate linguistic annotation of ASL video data, enabling the user to directly look up a sign in the video being annotated, and, upon confirmation of the match, to directly enter into the annotation the gloss and features of that sign, greatly increasing the efficiency and consistency of linguistic annotations of ASL video data.
Fusing Gathers with Integer Linear Programming
Venue: arXiv cs.PL 2024
Year: 2024

We present an Integer Linear Programming based approach to finding the optimal fusion strategy for combinator-based parallel programs. While combinator-based languages or libraries provide a convenient interface for programming parallel hardware, fusing combinators to more complex operations is essential to achieve the desired performance. Our approach is not only suitable for languages with the usual map, fold, scan, indexing and scatter operations, but also gather operations, which access arrays in arbitrary order, and therefore goes beyond the traditional producer-consumer fusion. It can be parametrised with appropriate cost functions, and is fast enough to be suitable for just-in-time compilation.
dzNLP at NADI 2024 Shared Task: Multi-Classifier Ensemble with Weighted Voting and TF-IDF Features
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents the contribution of our dzNLP team to the NADI 2024 shared task, specifically in Subtask 1 - Multi-label Country-level Dialect Identification (MLDID) (Closed Track). We explored various configurations to address the challenge: in Experiment 1, we utilized a union of n-gram analyzers (word, character, character with word boundaries) with different n-gram values; in Experiment 2, we combined a weighted union of Term Frequency-Inverse Document Frequency (TF-IDF) features with various weights; and in Experiment 3, we implemented a weighted major voting scheme using three classifiers: Linear Support Vector Classifier (LSVC), Random Forest (RF), and K-Nearest Neighbors (KNN). Our approach, despite its simplicity and reliance on traditional machine learning techniques, demonstrated competitive performance in terms of F1-score and precision. Notably, we achieved the highest precision score of 63.22% among the participating teams. However, our overall F1 score was approximately 21%, significantly impacted by a low recall rate of 12.87%. This indicates that while our models were highly precise, they struggled to recall a broad range of dialect labels, highlighting a critical area for improvement in handling diverse dialectal variations.
COMCAT: Leveraging Human Judgment to Improve Automatic Documentation and Summarization
Venue: arXiv cs.SE 2024
Year: 2024

Software maintenance constitutes a substantial portion of the total lifetime costs of software, with a significant portion attributed to code comprehension. Software comprehension is eased by documentation such as comments that summarize and explain code. We present COMCAT, an approach to automate comment generation by augmenting Large Language Models (LLMs) with expertise-guided context to target the annotation of source code with comments that improve comprehension. Our approach enables the selection of the most relevant and informative comments for a given snippet or file containing source code. We develop the COMCAT pipeline to comment C/C++ files by (1) automatically identifying suitable locations in which to place comments, (2) predicting the most helpful type of comment for each location, and (3) generating a comment based on the selected location and comment type. In a human subject evaluation, we demonstrate that COMCAT-generated comments significantly improve developer code comprehension across three indicative software engineering tasks by up to 12% for 87% of participants. In addition, we demonstrate that COMCAT-generated comments are at least as accurate and readable as human-generated comments and are preferred over standard ChatGPT-generated comments for up to 92% of snippets of code. Furthermore, we develop and release a dataset containing source code snippets, human-written comments, and human-annotated comment categories. COMCAT leverages LLMs to offer a significant improvement in code comprehension across a variety of human software engineering tasks.
DPDPU: Data Processing with DPUs
Venue: arXiv cs.DC 2024
Year: 2024

Improving the performance and reducing the cost of cloud data systems is increasingly challenging. Data processing units (DPUs) are a promising solution, but utilizing them for data processing needs characterizing the new hardware and recognizing their capabilities and constraints. We hence propose DPDPU, a platform for holistically exploiting DPUs to optimize data processing tasks that are critical to performance and cost. It seeks to fill the semantic gap between DPUs and data processing systems and handle DPU heterogeneity with three engines dedicated to compute, networking, and storage. This paper describes our vision, DPDPU's key components, their associated utilization challenges, as well as the current progress and future plans.
MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis
Venue: arXiv cs.CV 2024
Year: 2024

We present MeshSegmenter, a simple yet effective framework designed for zero-shot 3D semantic segmentation. This model successfully extends the powerful capabilities of 2D segmentation models to 3D meshes, delivering accurate 3D segmentation across diverse meshes and segment descriptions. Specifically, our model leverages the Segment Anything Model (SAM) model to segment the target regions from images rendered from the 3D shape. In light of the importance of the texture for segmentation, we also leverage the pretrained stable diffusion model to generate images with textures from 3D shape, and leverage SAM to segment the target regions from images with textures. Textures supplement the shape for segmentation and facilitate accurate 3D segmentation even in geometrically non-prominent areas, such as segmenting a car door within a car mesh. To achieve the 3D segments, we render 2D images from different views and conduct segmentation for both textured and untextured images. Lastly, we develop a multi-view revoting scheme that integrates 2D segmentation results and confidence scores from various views onto the 3D mesh, ensuring the 3D consistency of segmentation results and eliminating inaccuracies from specific perspectives. Through these innovations, MeshSegmenter offers stable and reliable 3D segmentation results both quantitatively and qualitatively, highlighting its potential as a transformative tool in the field of 3D zero-shot segmentation. The code is available at \url{https://github.com/zimingzhong/MeshSegmenter}.
Prover-Verifier Games improve legibility of LLM outputs
Venue: arXiv cs.CL 2024
Year: 2024

One way to increase confidence in the outputs of Large Language Models (LLMs) is to support them with reasoning that is clear and easy to check -- a property we call legibility. We study legibility in the context of solving grade-school math problems and show that optimizing chain-of-thought solutions only for answer correctness can make them less legible. To mitigate the loss in legibility, we propose a training algorithm inspired by Prover-Verifier Game from Anil et al. (2021). Our algorithm iteratively trains small verifiers to predict solution correctness, "helpful" provers to produce correct solutions that the verifier accepts, and "sneaky" provers to produce incorrect solutions that fool the verifier. We find that the helpful prover's accuracy and the verifier's robustness to adversarial attacks increase over the course of training. Furthermore, we show that legibility training transfers to time-constrained humans tasked with verifying solution correctness. Over course of LLM training human accuracy increases when checking the helpful prover's solutions, and decreases when checking the sneaky prover's solutions. Hence, training for checkability by small verifiers is a plausible technique for increasing output legibility. Our results suggest legibility training against small verifiers as a practical avenue for increasing legibility of large LLMs to humans, and thus could help with alignment of superhuman models.
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench
Venue: arXiv cs.CL 2024
Year: 2024

Recent advancements in Language Models (LMs) have catalyzed the creation of multiple benchmarks, designed to assess these models' general capabilities. A crucial task, however, is assessing the validity of the benchmarks themselves. This is most commonly done via Benchmark Agreement Testing (BAT), where new benchmarks are validated against established ones using some agreement metric (e.g., rank correlation). Despite the crucial role of BAT for benchmark builders and consumers, there are no standardized procedures for such agreement testing. This deficiency can lead to invalid conclusions, fostering mistrust in benchmarks and upending the ability to properly choose the appropriate benchmark to use. By analyzing over 40 prominent benchmarks, we demonstrate how some overlooked methodological choices can significantly influence BAT results, potentially undermining the validity of conclusions. To address these inconsistencies, we propose a set of best practices for BAT and demonstrate how utilizing these methodologies greatly improves BAT robustness and validity. To foster adoption and facilitate future research,, we introduce BenchBench, a python package for BAT, and release the BenchBench-leaderboard, a meta-benchmark designed to evaluate benchmarks using their peers. Our findings underscore the necessity for standardized BAT, ensuring the robustness and validity of benchmark evaluations in the evolving landscape of language model research. BenchBench Package: github.com/IBM/BenchBench Leaderboard: hf.co/spaces/IBM/BenchBench
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift
Venue: arXiv cs.CV 2024
Year: 2024

Studying adversarial attacks on artificial intelligence (AI) systems helps discover model shortcomings, enabling the construction of a more robust system. Most existing adversarial attack methods only concentrate on single-task single-model or single-task cross-model scenarios, overlooking the multi-task characteristic of artificial intelligence systems. As a result, most of the existing attacks do not pose a practical threat to a comprehensive and collaborative AI system. However, implementing cross-task attacks is highly demanding and challenging due to the difficulty in obtaining the real labels of different tasks for the same picture and harmonizing the loss functions across different tasks. To address this issue, we propose a self-supervised Cross-Task Attack framework (CTA), which utilizes co-attention and anti-attention maps to generate cross-task adversarial perturbation. Specifically, the co-attention map reflects the area to which different visual task models pay attention, while the anti-attention map reflects the area that different visual task models neglect. CTA generates cross-task perturbations by shifting the attention area of samples away from the co-attention map and closer to the anti-attention map. We conduct extensive experiments on multiple vision tasks and the experimental results confirm the effectiveness of the proposed design for adversarial attacks.
Cannabis Impairment Monitoring Using Objective Eye Tracking Analytics
Venue: arXiv cs.HC 2024
Year: 2024

The continuing growth in cannabis legalization necessitates the development of rapid, objective methods for assessing impairment to ensure public and occupational safety. Traditional measurement techniques are subjective, time-consuming, and do not directly measure physical impairment. This study introduces objective metrics derived from eye-tracking analytics to address these limitations. We employed a head-mounted display to present 20 subjects with smooth pursuit performance, horizontal saccade, and simple reaction time tasks. Individual and group performance was compared before and after cannabis use. Results demonstrated significant changes in oculomotor control post-cannabis consumption, with smooth pursuit performance showing the most substantial signal. The objective eye-tracking data was used to develop supervised learning models, achieving a classification accuracy of 89% for distinguishing between sober and impaired states when normalized against baseline measures. Eye-tracking is the optimal candidate for a portable, rapid, and objective tool for assessing cannabis impairment, offering significant improvements over current subjective and indirect methods.
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design
Venue: arXiv cs.IT 2024
Year: 2024

One of the most critical challenges for deploying distributed learning solutions, such as federated learning (FL), in wireless networks is the limited battery capacity of mobile clients. While it is a common belief that the major energy consumption of mobile clients comes from the uplink data transmission, this paper presents a novel finding, namely channel decoding also contributes significantly to the overall energy consumption of mobile clients in FL. Motivated by this new observation, we propose an energy-efficient adaptive channel decoding scheme that leverages the intrinsic robustness of FL to model errors. In particular, the robustness is exploited to reduce the energy consumption of channel decoders at mobile clients by adaptively adjusting the number of decoding iterations. We theoretically prove that wireless FL with communication errors can converge at the same rate as the case with error-free communication provided the bit error rate (BER) is properly constrained. An adaptive channel decoding scheme is then proposed to improve the energy efficiency of wireless FL systems. Experimental results demonstrate that the proposed method maintains the same learning accuracy while reducing the channel decoding energy consumption by ~20% when compared to an existing approach.
GAP9Shield: A 150GOPS AI-capable Ultra-low Power Module for Vision and Ranging Applications on Nano-drones
Venue: arXiv cs.RO 2024
Year: 2024

The evolution of AI and digital signal processing technologies, combined with affordable energy-efficient processors, has propelled the development of both hardware and software for drone applications. Nano-drones, which fit into the palm of the hand, are suitable for indoor environments and safe for human interaction; however, they often fail to deliver the required performance for complex tasks due to the lack of hardware providing sufficient sensing and computing performance. Addressing this gap, we present the GAP9Shield, a nano-drone-compatible module powered by the GAP9, a 150GOPS-capable SoC. The system also includes a 5MP OV5647 camera for high-definition imaging, a WiFi-BLE NINA module, and a 5D VL53L1-based ranging subsystem, which enhances obstacle avoidance capabilities. In comparison with similarly targeted state-of-the-art systems, GAP9Shield provides a 20% higher sample rate (RGB images) while offering a 20% weight reduction. In this paper, we also highlight the energy efficiency and processing power capabilities of GAP9 for object detection (YOLO), localization, and mapping, which can run within a power envelope of below 100 mW and at low latency (as 17 ms for object detection), highlighting the transformative potential of GAP9 for the new generation of nano-drone applications.